Shahraki Parisa Khodabandeh, Feizi Awat, Aminorroaya Sima, Ghanbari Heshmatollah, Abyar Majid, Amini Massoud, Aminorroaya Ashraf
Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran.
J Res Med Sci. 2025 Mar 29;30:17. doi: 10.4103/jrms.jrms_139_23. eCollection 2025.
We aimed to develop risk models for predicting the onset of developing diabetes and prediabetes in the first-degree relatives (FDRs) of patients with type 2 diabetes, who have normal glucose tolerance (NGT).
In this study, 1765 FDRs of patients with type 2 diabetes mellitus, who had NGT, were subjected to the statistical analysis. Diabetes risk factors, including anthropometric indices, physical activity, fast plasma glucose, plasma glucose concentrations 2-h after oral glucose administration, glycosylated hemoglobin (HbA1c), blood pressure, and lipid profile at the baseline were considered as independent variables. Kaplan-Meier, log-rank test, univariate, and multivariable proportional hazard Cox regression were used for the data analysis. The optimal cutoff value for risk score was created according to the receiver operating characteristic curve analysis.
The best diabetes predictability was achieved by a model in which waist-to-hip ratio, HbA1c, oral glucose tolerance test-area under the curve (OGTT-AUC), and the lipid profile were included. The best prediabetes risk model included HbA1c, OGTT-AUC, systolic blood pressure, and the lipid profile. The predictive ability of multivariable risk models was compared with fasting plasma glucose (FPG), HbA1c, and OGTT. The predictive ability of developed models was higher than FPG and HbA1c; however, it was comparable with OGTT-AUC alone. In addition, our study showed that the developed models predicted diabetes and OGTT-AUC better than the Finnish Diabetes Risk Score (FINDRISC).
We recommend regular monitoring of risk factors for the FDRs of patients with type 2 diabetes as an efficient approach for predicting and prevention of the occurrence of diabetes and prediabetes in future. Our developed diabetes risk score models showed precise prediction ability compared to the FINDRISC in Iranian population.
我们旨在为2型糖尿病患者的一级亲属（FDRs）建立风险模型，这些亲属具有正常糖耐量（NGT），用于预测糖尿病和糖尿病前期的发病情况。
在本研究中，对1765名2型糖尿病患者的具有NGT的FDRs进行了统计分析。将糖尿病风险因素，包括人体测量指标、身体活动、空腹血糖、口服葡萄糖后2小时血浆葡萄糖浓度、糖化血红蛋白（HbA1c）、血压和基线时的血脂谱作为自变量。采用Kaplan-Meier法、对数秩检验、单变量和多变量比例风险Cox回归进行数据分析。根据受试者工作特征曲线分析确定风险评分的最佳临界值。
通过纳入腰臀比、HbA1c、口服葡萄糖耐量试验曲线下面积（OGTT-AUC）和血脂谱的模型实现了最佳的糖尿病预测能力。最佳的糖尿病前期风险模型包括HbA1c、OGTT-AUC、收缩压和血脂谱。将多变量风险模型的预测能力与空腹血糖（FPG）、HbA1c和OGTT进行了比较。所建立模型的预测能力高于FPG和HbA1c；然而，它与单独的OGTT-AUC相当。此外，我们的研究表明，所建立的模型在预测糖尿病和OGTT-AUC方面优于芬兰糖尿病风险评分（FINDRISC）。
我们建议对2型糖尿病患者的FDRs定期监测风险因素，作为未来预测和预防糖尿病及糖尿病前期发生的有效方法。与伊朗人群中的FINDRISC相比，我们建立的糖尿病风险评分模型显示出精确的预测能力。
